Guidance for setting international standards on reporting longitudinal adherence to stool-based colorectal cancer screening
Bibliographic record
Abstract
Longitudinal adherence to colorectal cancer (CRC) screening is reported using different summarizing measures, which hampers international comparison. We provide evidence to guide recommendations on which longitudinal adherence measure to report. Using adherence data over four stool-based CRC screening rounds in three countries, we calculated six summarizing adherence measures; adherence over all rounds, adherence per round, rescreening, full programme adherence (yes/no), regularity (never/inconsistent/consistent screenees) and number of times participated. For each measure, we calculated the accuracy in capturing the observed adherence patterns. Using the ASCCA model, we predicted screening effectiveness when using summarizing measures as model input versus the observed adherence patterns. Adherence over all rounds in the Italian, Spanish and Dutch cohorts was 64.9%, 42.8% and 61.5%, respectively, and the proportion of consistent screenees was 50.9%, 26.3% and 45.7%. Number of times participated and regularity were most accurate and resulted in similar model-predicted screening effectiveness as simulating the observed adherence patterns of Italy, Spain and the Netherlands (mortality reductions: 24.4%, 16.9% and 23.5%). Adherence over all rounds and adherence per round were least accurate. Screening effectiveness was overestimated when using adherence over all rounds (mortality reductions: 26.8%, 19.4% and 25.7%) and adherence per round (mortality reductions: 26.8%, 19.5% and 25.9%). To conclude, number of times participated and regularity were most accurate and resulted in similar model-predicted screening effectiveness as using the observed adherence patterns. However they require longitudinal data. To facilitate international comparison of CRC screening programme performance, consensus on an accurate adherence measure to report should be reached.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.360 | 0.549 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.016 | 0.011 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".